Stream Learning:
In stream learning, data arrive sequentially and their distributions can change over time, the so-called concept drifts [1]}. Concept drifts can be handled explicitly through informed adaptation, where the model adapts only if a change has been detected, or implicitly through blind adaptation, where the model is updated constantly to account for changes in the underlying data distributions. In addition, models developed for stream learning are categorized as incremental and online [2]}. Incremental models are trained in batches [3]}, with the help of a chunk (window) of instances, while online models are updated continuously (per instance) to accommodate newly incoming examples [4]}.
The goal of stream classification is to maintain a valid classifier over the stream as new data arrive and old data become outdated. The maintenance depends on the underlying learning model, e.g., Naive Bayes classifiers [5]}, Decision Trees [6]}, [7]}, KNN [8]}.
